Antennas may be classified as either single element or array antennas. Single element antennas may be either onmidirectional or directional. Array antennas may be classified as phased arrays or adaptive arrays. Onmidirectional antennas have equal gain in all directions and are also known as isotropic antennas. Directional antennas have more gain in certain directions and less in others. In general, an antenna may be omnidirectional in one plane (e.g. azimuth) while being directional in another (e.g. elevation). A phased array antenna system uses an array of simple ominidirectional or directional element antennas and combines the signal induced on the antennas to form an array output.
Such an antenna system typically controls the direction where maximum gain appears by adjusting the electrical phase difference between different antenna elements. An adaptive antenna is another type of antenna array that also combines the outputs of an) array of antenna elements, but controls the directional gain of the antenna by adjusting both phase and amplitude of the signal at each individual element. As described above, the array processor combines the signals induced on different elements to form a single output of the array. In the receive, or uplink, direction, the induced signals are produced by electromagnetic waves created by remote sources or transmitters and the signals are combined electronically. In the transmit, or downlink, direction, the induced signals are produced by individual transmitter elements feeding the array and the signals are combined in space through the coherent addition of electromagnetic waves.
A plot of the array response as a function of angle (azimuth and/or elevation) is normally referred to as the array pattern or beam pattern. The process of combining signals from different elements is known as beamforming. In general, beamforming creates both maximal gain in one or more directions and minimal gain or pattern nulls in others. Directional antennas in essence employ static beam forming, where the desired beam pattern is determined in advance of the antenna design and installation, and is typically not changed once implemented. Beam steering with directional antennas is typically performed mechanically by steering the entire antenna platform. Adaptive beam forming techniques permit the host system to change the antenna beam pattern electronically through the use of adaptive arrays or phased arrays in real time in response to changes in environment or system requirements.
Adaptive beam forming systems can be classified as either a radio frequency (“RF”) adaptive beamformer (“RFAB”) or a digital adaptive beamformer (“DABF”). An RFAB system adjusts the phases and/or amplitudes of signals at the RF or intermediate frequency (“IF”) stage of the transmitter and/or receiver chain associated with the antenna. The number of beam patterns which a RFAB can produce is limited practically by the number of electrical attenuators and phase shifters available prior to the antenna terminals. A DABF system adjusts the phases and/or amplitudes of signals digitally. In a DABF receiver, signals are processed after they have been converted to IF or separated into baseband in-phase and quadrature components and digitally sampled, but prior to demodulation. In a DABF transmitter, signals are processed after modulation, but prior to digital-to-analog conversion.
Because beam forming is done digitally and is not limited practically by the number of RF or IF attenuators or phase shifters, separate signal channels can be established for each remote source or receiver of interest. Sources may include, for example, a user in the case of a wireless radio access network, a target in the case of a radar system, a jammer in the case of an electronic countermeasures (“ECM”) or electronic counter-countermeasures (“ECCM”) system, or some source, in the case of an electronic warfare (“EW”) system. A receiver may also be any one of these.
DABF algorithms can be classified in terms of the following features, including, for example, whether the signal angle-of-arrival (“AOA”) must be known; whether or not the algorithm functions in the presence of interference and if so, what number of interferers can be supported; whether the interferer's AOAs must be known; whether the number of interferers is known or unknown; and special characteristics required of the signal. A survey of available beamforming algorithms is shown in Table 1. When the signal and/or interferer AOA is required, an additional set of AOA-estimation techniques are available. The most popular techniques in this class include the Multiple Signal Classification (MUSIC) technique, Root-MUSIC, and ESPRIT.
TABLE 1Survey of Beamforming AlgorithmsNumber ofParameterInterferersSignalSpecial SignalTechniqueInterferersOptimizedAOAAOACharacteristicsDelay-and-NoneSNRKnownKnownsumDICANNE1 or moreSignal(unknown)PowerNAMI,1 or moreSINRUnknownKnownSPNAMI(unknown)MMSE, LMS,1 or moreReferenceUnknownUnknownReferenceRLS, MRIII(unknown)MaximumSignalLikelihoodRequiredEigenstructure1 or moreSINRUnknownUnknownmethods(known)CMA1 or moreSignalUnknownUnknownRequires(unknown)MSEsignal to beconstantmagnitude
(The Number of Interferers Assumed to be Less Than L−2 in All Cases, Where L is the Number of Antenna Elements)
One constraint on all of the existing techniques is the requirement that the number of sources be less than the number of antenna elements. Without this constraint, the optimization problem would amount to an unconstrained problem where the number of equations (i.e. the number of antenna element signals) would be less than the number of unknowns (i.e. the number of user signals)—a problem with no closed-form solution.
While some antenna element weights would produce higher signal-to-interference-and-noise ratio (“SINR”) values than other weights, it is difficult if not impossible to determine which weight sets are relatively better than others without some sort of exhaustive search over the entire set of possible antenna weights. The dimensionality of such a search is daunting however—for an 8-element array with phase and amplitude weight factors encoded with 8-bits, the number of possible weight sets is 2128 (1034).
Genetic algorithms have been applied successfully to a variety of problems with highly dimensional solution spaces rich in local extrema. Genetic algorithms work by encoding potential solutions in data objects referred to as chromosomes, which are then manipulated using genetic operations such as crossover, mutation, and inversion in response to some measure of fitness of each trial solution. Additional genetic operators include segregation, translocation, duplication, deletion, sexual determination, differentiation and speciation.
Genetic algorithms have been applied in a number of antenna design problems. Typically, the algorithm is applied to optimize some single or multi-variate cost function, such as gain or sidelobe level. Haupt has described an adaptive method whereby nulls are placed in a received antenna pattern in such a way as to minimize interference signal power (see FIG. 1). Haupt's method employed a conventional phased array with digital phase shifters, but with the least significant bits (“LSB”) of the phase shifters controlled using a genetic algorithm.
For example, U.S. Pat. No. 6,175,331 to Woodsum et al. (“Woodsum”), entitled “Method and Apparatus for Determining and Forming Delayed Waveforms for Forming Radio Frequency Transmitting or Receiving Beams for An Array of Radio Frequency Transmitting or Receiving Elements,” discloses applying genetic algorithms to a phased array system where the number of antenna elements exceeds the number of user signal channels (see FIG. 2). Woodsum's methods assume that the ideal beam pattern and phase shifts for each antenna element are known, but that only a limited number of phase shifts are available. The methods disclosed employ genetic algorithms to assign antenna elements to the set of available phase shifts such that the sum of the squared differences between the ideal and assigned phase shifts is minimized. In addition, U.S. patent application Ser. No. 09/629,386 to G. J. Zancewicz, entitled ‘Genetic Adaptive Antenna Array Processor’ (“386 application”) discloses a general adaptive array processor that controls both phase and amplitude and incorporates an additional genetic operator—inversion.
Finally, Weile and Michielssen, in “The Control of Adaptive Antenna Arrays with genetic Algorithms Using Dominance and Diploidy,” IEEE Transactions on Antennas and Propagation, vol. 49, 2001, pp. 1424–1433, have recently described a method whereby a genetic algorithm employing chromosome pairs—called diploid chromosomes—is used to control a linear phased array in a receiving system such that the desired SINR is maximized—the so-called Applebaum Optimization Criterion. See FIG. 3.